Research suggests that, on average, 50% of older adults have difficulty taking medications as prescribed and poor adherence to pharmacological therapies is a $100 billion healthcare problem in the United States. Older adults are an important user group to study when it comes to medication adherence for at least 4 reasons: 1) they take more medications than their younger counterparts and have significantly more adherence problems, 2) the number of older adults in this country is rapidly increasing making them 1 of the largest user population, 3) medication adherence is a critical activity of daily living activities, 4) older adults have unique needs due to age-related declines in sensory-perceptual, movement control, and cognitive abilities. A recent comprehensive review of medication adherence noted that a primary factor identified with poor adherence was cognitive impairment, with complexity of the medication regimen a second factor. There is at present no scale of medication regime complexity or assessment of medication taking competence. This lack of information results in deficient decision making and contributes to poor healthcare. This Phase I SBIR proposal has 3 primary goals. First, to develop a computerized test-MedicationIQ test-that will reliably identify whether older individuals may be experiencing cognitive difficulties that place them at risk for medication adherence. Second, to develop a MedicationComplexityMetric to predict the interaction of medication regime complexity, mental competence and nature of medication administration support. Third, to develop Medication Compliance Optimization algorithms to minimize the cognitive demands of a medication regime and maximize adherence. We will develop a technology for medication compliance screening so in the future, as patients medication regime complexity increases and/or cognitive function declines, the MedicationlQ test will become routine to aid health care management. Such screening criteria are common in PCP practices (e.g., screens for breast or prostate cancer based on age and medical history). The MedicationComplexityMetric will provide the health care professional with predictive norms relating to the complexity of the medication regime and the Medication Compliance Optimization algorithms will identify how modifications in the medication regime might improve medication adherence. [unreadable] [unreadable]